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#104
@kilometer00
2023.03.04
BeginneR Session
Data processing &
visualization
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Who！？
Who?
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Who！？
名前： 三村 @kilometer
職業： ポスドク (こうがくはくし)
専⾨： ⾏動神経科学(霊⻑類)
脳イメージング
医療システム⼯学
R歴： ~ 10年ぐらい
流⾏: 椅⼦を新調
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宣伝!!（書籍の翻訳に参加しました。）
絶賛販売中！
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BeginneR Session
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BeginneR
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Before A'er
BeginneR Session
BeginneR BeginneR
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BeginneR Advanced Hoxo_m
If I have seen further it is by standing on the
shoulders of Giants.
-- Sir Isaac Newton, 1676
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#104
@kilometer00
2023.03.03
BeginneR Session
Data processing &
visualization
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import Tidy
Transform
Visualize
Model
Communicate
Modified from “R for Data Science”, H. Wickham, 2017
Data Science
①
②
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σʔλ
情報のうち意思伝達・解釈・処理に
適した再利⽤可能なもの
国際電気標準会議(Interna@onal Electrotechnical Commission, IEC)による定義
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σʔλ
情報のうち意思伝達・解釈・処理に
適した再利⽤可能なもの
ใ 実存を符号化した表象
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σʔλ
ใͷ͏ͪҙࢥୡɾղऍɾॲཧʹ
దͨ͠࠶ར༻Մೳͳͷ
ใ ࣮ଘΛූ߸Խͨ͠ද
࣮ଘ
؍ͷ༗ແʹΑΒͣଘࡏ͍ͯ͠Δ
ͷͦͷͷ
ࣸ૾ʢූ߸Խʣ
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集合! 集合"
要素# 要素$
写像 %: ! → "もしくは%: # ⟼ $
（始集合・定義域） （終集合・終域）
【写像】
ある集合の要素を他の集合のただ1つの要素に
対応づける規則
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地図空間
⽣物種名空間
名空間
⾦銭価値空間
(円)
⾦銭価値空間
(ドル)
コーヒー
¥290
$2.53
[緯度, 経度]
Homo sapiens
実存
写像
写像
写像
写像
写像
写像
情報
【写像】
ある集合の要素を他の集合のただ1つの要素に対応づける規則
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ࣸ૾
Ϧϯΰ
ʢ࣮ଘʣ
Ϧϯΰ
ʢใʣ
mapping
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ใྔ
࣮ଘ
ใ
σʔλ Ϧϯΰ
ූ߸Խ
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ใྔ
࣮ଘ
ใ
σʔλ Ϧϯΰ
ූ߸Խ
ใྔͷଛࣦ
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Experiment
hypothesis observa=on
principle phenotype
model data
Truth
Knowledge f X
(unknown)
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raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
Nested form
pivot_wider()
Plot
group_nest() unnest()
{ggplot2}
{patchwork}
Image Files
ggsave()
Data Processing
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data.frame
*bble
raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
Nested form
pivot_wider()
Plot
group_nest() unnest()
{ggplot2}
{patchwork}
Image Files
ggsave()
Data Processing
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data.frame
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vector
in Excel
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vector
in R
in Excel
pre <- c(1, 2, 3, 4, 5)
post <- pre * 5
> pre
[1] 1 2 3 4 5
> post
[1] 5 10 15 20 25
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vector
vec1 <- c(1, 2, 3, 4, 5)
vec2 <- 1:5
vec3 <- seq(from = 1, to = 5, by = 1)
> vec1
[1] 1 2 3 4 5
> vec2
[1] 1 2 3 4 5
> vec3
[1] 1 2 3 4 5
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vector
vec1 <- seq(from = 1, to = 5, by = 1)
vec2 <- seq(1, 5, 1)
> vec1
[1] 1 2 3 4 5
> vec2
[1] 1 2 3 4 5
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> ?seq
vector
seq{base}
Sequence Generation
Description
Generate regular sequences. seq is a standard
generic with a default method. …
Usage
seq(...)
## Default S3 method:
seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),
length.out = NULL, along.with = NULL, ...)
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vector
vec1 <- rep(1:3, times = 2)
vec2 <- rep(1:3, each = 2)
vec3 <- rep(1:3, times = 2, each = 2)
> vec1
[1] 1 2 3 1 2 3
> vec2
[1] 1 1 2 2 3 3
> vec3
[1] 1 1 2 2 3 3 1 1 2 2 3 3
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vector
vec1 <- 11:15
> vec1
[1] 11 12 13 14 15
> vec1[1]
[1] 11
> vec1[3:5]
[1] 13 14 15
> vec1[c(1:2, 5)]
[1] 11 12 15
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list
list1 <- list(1:6, 11:15, c("a", "b", "c"))
> list1
[[1]]
[1] 1 2 3 4 5 6
[[2]]
[1] 11 12 13 14 15
[[3]]
[1] "a" "b" "c"
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list
list1 <- list(1:6, 11:15, c("a", "b", "c"))
> list1[[1]]
[1] 1 2 3 4 5 6
> list1[[3]][2:3]
[1] "b" "c"
> list1[[2]] * 3
[1] 33 36 39 42 45
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named list
list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
> list2
$A
[1] 1 2 3 4 5 6
$B
[1] 11 12 13 14 15
$C
[1] "a" "b" "c"
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> list2$A
[1] 1 2 3 4 5 6
> list2$C[2:3]
[1] "b" "c"
> list2$B * 3
[1] 33 36 39 42 45
named list
list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
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list1 <- list(1:6, 11:15, c("a", "b", "c"))
> class(list1)
[1] "list"
> names(list1)
NULL
list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
> class(list2)
[1] "list"
> names(list2)
[1] "A" "B" "C"
named list
list
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list3 <- list(A = 1:3, B = 11:13)
> class(list3)
[1] "list"
> names(list3)
[1] "A" "B"
df1 <- data.frame(A = 1:3, B = 11:13)
> class(df1)
[1] "data.frame"
> names(df1)
[1] "A" "B"
named list & data.frame
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> str(list3)
List of 2
$ A: int [1:3] 1 2 3
$ B: int [1:3] 11 12 13
> str(df1)
'data.frame': 3 obs. of 2 variables:
$ A: int 1 2 3
$ B: int 11 12 13
list3 <- list(A = 1:3, B = 11:13)
df1 <- data.frame(A = 1:3, B = 11:13)
named list & data.frame
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> list3
$A
[1] 1 2 3
$B
[1] 11 12 13
> df1
A B
1 1 11
2 2 12
3 3 13
named list & data.frame
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data.frame vs. matrix
A B
1 1 11
2 2 12
3 3 13
[,1] [,2]
[1,] 1 11
[2,] 2 12
[3,] 3 13
df1 <- data.frame(A = 1:3, B = 11:13)
> str(mat1)
int [1:3, 1:2] 1 2 3 11 12 13
> str(df1)
'data.frame': 3 obs. of 2 vars.:
$ A: int 1 2 3
$ B: int 11 12 13
mat1 <- matrix(c(1:3, 11:13), nrow = 3, ncol = 2)
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data.frame
variables
observa*on
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data.frame
*bble
raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
Nested form
pivot_wider()
Plot
group_nest() unnest()
{ggplot2}
{patchwork}
Image Files
ggsave()
Data Processing
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> anscombe
x1 x2 x3 x4 y1 y2 y3 y4
1 10 10 10 8 8.04 9.14 7.46 6.58
2 8 8 8 8 6.95 8.14 6.77 5.76
3 13 13 13 8 7.58 8.74 12.74 7.71
4 9 9 9 8 8.81 8.77 7.11 8.84
5 11 11 11 8 8.33 9.26 7.81 8.47
6 14 14 14 8 9.96 8.10 8.84 7.04
7 6 6 6 8 7.24 6.13 6.08 5.25
8 4 4 4 19 4.26 3.10 5.39 12.50
9 12 12 12 8 10.84 9.13 8.15 5.56
10 7 7 7 8 4.82 7.26 6.42 7.91
11 5 5 5 8 5.68 4.74 5.73 6.89
Wide form data
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> df
tag x1 x2 x3 x4 y1 y2 y3 y4
1 1 10 10 10 8 8.04 9.14 7.46 6.58
2 2 8 8 8 8 6.95 8.14 6.77 5.76
3 3 13 13 13 8 7.58 8.74 12.74 7.71
4 4 9 9 9 8 8.81 8.77 7.11 8.84
5 5 11 11 11 8 8.33 9.26 7.81 8.47
6 6 14 14 14 8 9.96 8.10 8.84 7.04
Wide form data
df <-
rownames_to_column(
anscombe,
var = "tag"
)
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Wide form → Long form data
df_long_1 <-
pivot_longer(
data = df,
cols = !tag
)
df_long_2 <-
pivot_longer(
data = df,
cols = !tag,
names_to = c(".value", "key"),
names_pattern = c("(.)(.)")
)
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Long form → Wide form data
pivot_wider(
data = df_long_1,
values_from = value,
names_from = name
)
pivot_wider(
data = df_long_2,
values_from = c(x, y),
names_from = tag
)
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data.frame / *bble
raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
pivot_wider()
Plot
{ggplot2}
Image Files
ggsave()
Data Processing
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raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
pivot_wider()
Plot
{ggplot2}
Image Files
ggsave()
Data Processing
Long form
Long form
Long form
Long form
Long form
Long form
Long form
Long form
data.frame / *bble
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vignette("dplyr")
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It (dplyr) provides simple “verbs” to help
you translate your thoughts into code.
func?ons that correspond to the most
common data manipula?on tasks
Introduc6on to dplyr
h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html
WFSCT {dplyr}
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dplyrは、あなたの考えをコードに翻訳
するための【動詞】を提供する。
データ操作における基本のキを、
シンプルに実⾏できる関数 (群)
Introduc6on to dplyr
h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html
WFSCT {dplyr}
※ かなり意訳
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(SBNNBSPGEBUBNBOJQVMBUJPO
By constraining your op@ons,
it helps you think about your data
manipula@on challenges.
Introduc6on to dplyr
hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
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選択肢を制限することで、
データ解析のステップを
シンプルに考えられますヨ。
（めっちゃ意訳）
Introduc6on to dplyr
hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
※ まさに意訳
(SBNNBSPGEBUBNBOJQVMBUJPO
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1. mutate()
2. filter()
3. select()
4. group_by()
5. summarize()
6. left_join()
7. arrange()
Data.frame manipula@on
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1. mutate()
2. filter()
3. select()
4. group_by()
5. summarize()
6. left_join()
7. arrange()
Data.frame manipula@on
0. %>%
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1JQFBMHFCSB
X %>% f
X %>% f(y)
X %>% f %>% g
X %>% f(y, .)
f(X)
f(X, y)
g(f(X))
f(y, X)
%>% {magri7r}
「dplyr再⼊⾨（基本編）」yutanihila@on
h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
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①
②
③
④
lift
take
pour
put
Bring milk from the kitchen!
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①
lift
Bring milk from the kitchen!
lift(Robot, glass, table) -> Robot'
take
②
take(Robot', fridge, milk) -> Robot''
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Bring milk from the kitchen!
Robot' <- lift(Robot, glass, table)
Robot'' <- take(Robot', fridge, milk)
Robot''' <- pour(Robot'', milk, glass)
result <- put(Robot''', glass, table)
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
by using pipe,
# ①
# ②
# ③
# ④
# ①
# ②
# ③
# ④
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The =dyverse style guides
h"ps://style.;dyverse.org/syntax.html#object-names
"There are only two hard things in Computer Science:
cache invalida:on and naming things"
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Bring milk from the kitchen!
Robot' <- lift(Robot, glass, table)
Robot'' <- take(Robot', fridge, milk)
Robot''' <- pour(Robot'', milk, glass)
result <- put(Robot''', glass, table)
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
by using pipe,
# ①
# ②
# ③
# ④
# ①
# ②
# ③
# ④
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Robot' <- lift(Robot, glass, table)
Robot'' <- take(Robot', fridge, milk)
Robot''' <- pour(Robot'', milk, glass)
result <- put(Robot''', glass, table)
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
by using pipe,
# ①
# ②
# ③
# ④
# ①
# ②
# ③
# ④
Thinking Reading
Bring milk from the kitchen!
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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1JQFBMHFCSB
X %>% f
X %>% f(y)
X %>% f %>% g
X %>% f(y, .)
f(X)
f(X, y)
g(f(X))
f(y, X)
%>% {magri7r}
「dplyr再⼊⾨（基本編）」yutanihila@on
h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
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1. mutate()
2. filter()
3. select()
4. group_by()
5. summarize()
6. left_join()
7. arrange()
Data.frame manipula@on
0. %>%
✔
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WFSCT {dplyr}
mutate # カラムの追加
+
mutate(dat, C = fun(A, B))
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WFSCT {dplyr}
mutate # カラムの追加
+
dat %>% mutate(C = fun(A, B))
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WFSCT {dplyr}
filter # 行の絞り込み
dat %>% filter(tag %in% c(1, 3, 5))
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ブール演算⼦ Boolean Algebra
A == B A != B
George Boole
1815 - 1864
A | B A & B
A %in% B
# equal to # not equal to
# or # and
# is A in B?
wikipedia
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"a" != "b"
# is A in B?
ブール演算⼦ Boolean Algebra
[1] TRUE
1 %in% 10:100
# is A in B?
[1] FALSE
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George Boole
1815 - 1864
A Class-Room Introduc;on to Logic
h"ps://niyamaklogic.wordpress.com/c
ategory/laws-of-thoughts/
Mathema=cian
Philosopher
&
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WFSCT {dplyr}
select # カラムの選択
dat %>% select(tag, B)
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WFSCT {dplyr}
select # カラムの選択
dat %>% select("tag", "B")
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WFSCT {dplyr}
select # カラムの選択
dat %>% select("tag", "B")
dat %>% select(tag, B)
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WFSCT {dplyr}
# Select help func?ons
starts_with("s") ends_with("s")
contains("se") matches("^.e")
one_of(c(”tag", ”B"))
everything()
hFps://kazutan.github.io/blog/2017/04/dplyr-select-memo/
「dplyr::selectの活⽤例メモ」kazutan
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1. mutate()
2. filter()
3. select()
4. group_by()
5. summarize()
6. left_join()
7. arrange()
Data.frame manipula@on
0. %>%
✔
✔
✔
✔
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選択肢を制限することで、
データ解析のステップを
シンプルに考えられますヨ。
（めっちゃ意訳）
Introduc6on to dplyr
hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
※ まさに意訳
(SBNNBSPGEBUBNBOJQVMBUJPO
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より多くの制約を課す事で、
魂の⾜枷から、より⾃由になる。
Igor Stravinsky
И@горь Ф Страви́нский
The more constraints one imposes,
the more one frees one's self of the
chains that shackle the spirit.
1882 - 1971
※ 割と意訳
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import Tidy
Transform
Visualize
Model
Communicate
Modified from “R for Data Science”, H. Wickham, 2017
Data Science
①
②
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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Text Image
Information
Intention
Data
decode
encode
Data analysis
feedback
≠
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Text
Image
First, A. Next, B.
Then C. Finally D.
?me
Intention
encode
"Frozen" structure
A B C D Xme
value
α
β
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ࣸ૾
Ϧϯΰ
ʢ࣮ଘʣ
Ϧϯΰ
ʢใʣ
mapping
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Ϧϯΰ
ࣸ૾
ϑϧʔπ
৭
ը૾
࣮ଘ ใ
νϟωϧ
mapping
channel
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#
$
%!
&!
%"
&"
# $
&!
&"
%!
%"
σʔλՄࢹԽ
ࣸ૾
mapping
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#
$
%!
&!
%"
&"
# $
&!
&"
%!
%"
σʔλՄࢹԽ
ࣸ૾
mapping
x axis, y axis, color, fill,
shape, linetype, alpha…
aesthetic channels
৹ඒతνϟωϧ
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#
$
%!
&!
%"
&"
# $
&!
&"
%!
%"
σʔλՄࢹԽ
ࣸ૾
mapping
x axis, y axis, color, fill,
shape, linetype, alpha…
aesthetic channels
৹ඒతνϟωϧ
ggplot(data = my_data) +
aes(x = X, y = Y)) +
goem_point()
HHQMPUʹΑΔ࡞ਤ
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࣮ଘ
ࣸ૾ʢ؍ʣ
σʔλ
ࣸ૾ʢσʔλՄࢹԽʣ
άϥϑ
!
"
#!
$!
#"
$"
# $
&!
&"
%!
%"
EBUB
mapping
aesthetic channels
৹ඒతνϟωϧ
σʔλՄࢹԽ
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ॳΊͯͷHHQMPU
library(tidyverse)
dat <-
data.frame(tag = rep(c("a", "b"), each = 2),
X = c(1, 3, 5, 7),
Y = c(3, 9, 4, 2))
ggplot() +
geom_point(data = dat,
mapping = aes(x = X, y = Y))
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ॳΊͯͷHHQMPU
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ॳΊͯͷHHQMPU
library(tidyverse)
dat <-
data.frame(tag = rep(c("a", "b"), each = 2),
X = c(1, 3, 5, 7),
Y = c(3, 9, 4, 2))
ggplot() +
geom_point(data = dat,
mapping = aes(x = X, y = Y))
EBUBGSBNFͷࢦఆ
BFT
ؔͷதͰ৹ඒతཁૉͱͯ͠มͱνϟωϧͷରԠΛࢦఆ
ඳը։࢝Λએݴ ه߸Ͱͭͳ͙
BFT
ؔͷҾ໊
EBUͷม໊
άϥϑͷछྨʹ߹ΘͤͨHFPN@
ؔΛ༻
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library(tidyverse)
dat <-
data.frame(tag = rep(c("a", "b"), each = 2),
X = c(1, 3, 5, 7),
Y = c(3, 9, 4, 2))
ggplot() +
geom_point(data = dat,
mapping = aes(x = X, y = Y)) +
geom_path(data = dat,
mapping = aes(x = X, y = Y))
ॳΊ͔ͯΒ൪ͷHHQMPU
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ॳΊ͔ͯΒ൪ͷHHQMPU
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HHQMPUίʔυͷॻ͖ํͷ৭ʑ
ggplot() +
geom_point(data = dat,
mapping = aes(x = X, y = Y)) +
geom_path(data = dat,
mapping = aes(x = X, y = Y))
ggplot(data = dat,
mapping = aes(x = X, y = Y)) +
geom_point() +
geom_path()
ggplot(data = dat) +
aes(x = X, y = Y) +
geom_point() +
geom_path()
ڞ௨ͷࢦఆΛHHQMPU
ؔͷதͰߦ͍ɺҎԼলུ͢Δ͜ͱ͕Մೳ
NBQQJOHͷใ͕ॻ͔ΕͨBFT
ؔΛHHQMPU
ؔͷ֎ʹஔ͘͜ͱͰ͖Δ
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HHQMPUίʔυͷॻ͖ํͷ৭ʑ
ggplot() +
geom_point(data = dat,
mapping = aes(x = X, y = Y, color = tag)) +
geom_path(data = dat,
mapping = aes(x = X, y = Y))
ggplot(data = dat) +
aes(x = X, y = Y) + # 括り出すのは共通するものだけ
geom_point(mapping = aes(color = tag)) +
geom_path()
ϙΠϯτͷ৭ͷNBQQJOHΛࢦఆ
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HHQMPUίʔυͷॻ͖ํͷ৭ʑ
ggplot(data = dat) +
aes(x = X, y = Y) +
geom_point(aes(color = tag)) +
geom_path()
ggplot(data = dat) +
aes(x = X, y = Y) +
geom_path() +
geom_point(aes(color = tag))
͋ͱ͔ΒͰॏͶͨཁૉ͕લ໘ʹඳը͞ΕΔ
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library(tidyverse)
dat <-
data.frame(tag = rep(c("a", "b"), each = 2),
X = c(1, 3, 5, 7),
Y = c(3, 9, 4, 2))
g <-
ggplot(data = dat) +
aes(x = X, y = Y) +
geom_path() +
geom_point(mapping = aes(color = tag))
HHQMPUը૾ͷอଘ
ggsave(filename = "fig/demo01.png",
plot = g,
width = 4, height = 3, dpi = 150)
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library(tidyverse)
dat <-
data.frame(tag = rep(c("a", "b"), each = 2),
X = c(1, 3, 5, 7),
Y = c(3, 9, 4, 2))
g <-
ggplot(data = dat) +
aes(x = X, y = Y) +
geom_path() +
geom_point(mapping = aes(color = tag))
HHQMPUը૾ͷอଘ
ggsave(filename = "fig/demo01.png",
plot = g,
width = 4, height = 3, dpi = 150)
αΠζσϑΥϧτͰΠϯν୯ҐͰࢦఆ
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library(tidyverse)
dat <-
data.frame(tag = rep(c("a", "b"), each = 2),
X = c(1, 3, 5, 7),
Y = c(3, 9, 4, 2))
g <-
ggplot(data = dat) +
aes(x = X, y = Y) +
geom_path() +
geom_point(mapping = aes(color = tag))
HHQMPUը૾ͷอଘ
ggsave(filename = "fig/demo01.png",
plot = g,
width = 10, height = 7.5, dpi = 150,
units = "cm") # "cm", "mm", "in"を指定可能
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HFNP@
ؔ܈ DGIUUQTXXXSTUVEJPDPNSFTPVSDFTDIFBUTIFFUT
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ෳͷܥྻΛඳը͢Δ
> head(anscombe)
x1 x2 x3 x4 y1 y2 y3 y4
1 10 10 10 8 8.04 9.14 7.46 6.58
2 8 8 8 8 6.95 8.14 6.77 5.76
3 13 13 13 8 7.58 8.74 12.74 7.71
4 9 9 9 8 8.81 8.77 7.11 8.84
5 11 11 11 8 8.33 9.26 7.81 8.47
6 14 14 14 8 9.96 8.10 8.84 7.04
ggplot(data = anscombe) +
geom_point(aes(x = x1, y = y1)) +
geom_point(aes(x = x2, y = y2), color = "Red") +
geom_point(aes(x = x3, y = y3), color = "Blue") +
geom_point(aes(x = x4, y = y4), color = "Green")
͜Ε·ͰͷࣝͰؤுΔͱ͜͏ͳΔ
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HHQMPUʹΑΔσʔλՄࢹԽ
࣮ଘ
ࣸ૾ʢ؍ʣ
σʔλ
ࣸ૾ʢσʔλՄࢹԽʣ
άϥϑ
!
"
#!
$!
#"
$"
SBXEBUB
写像
aesthetic channels
৹ඒతνϟωϧ
ՄࢹԽʹదͨ͠EBUBܗࣜ
変形
ਤͷͭͷ৹ඒతνϟωϧ͕
σʔλͷͭͷมʹରԠ͍ͯ͠Δ
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> head(anscombe)
x1 x2 x3 x4 y1 y2 y3 y4
1 10 10 10 8 8.04 9.14 7.46 6.58
2 8 8 8 8 6.95 8.14 6.77 5.76
3 13 13 13 8 7.58 8.74 12.74 7.71
4 9 9 9 8 8.81 8.77 7.11 8.84
5 11 11 11 8 8.33 9.26 7.81 8.47
6 14 14 14 8 9.96 8.10 8.84 7.04
> head(anscombe_long)
key x y
1 1 10 8.04
2 2 10 9.14
3 3 10 7.46
4 4 8 6.58
5 1 8 6.95
6 2 8 8.14
ggplot(data = anscombe_long) +
aes(x = x, y = y, color = key) +
geom_point()
৹ඒతνϟωϧ Y࣠
Z࣠
৭
ʹରԠ͢ΔมʹͳΔΑ͏มܗ
ݟ௨͠ྑ͘γϯϓϧʹՄࢹԽͰ͖Δ
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> head(anscombe)
x1 x2 x3 x4 y1 y2 y3 y4
1 10 10 10 8 8.04 9.14 7.46 6.58
2 8 8 8 8 6.95 8.14 6.77 5.76
3 13 13 13 8 7.58 8.74 12.74 7.71
4 9 9 9 8 8.81 8.77 7.11 8.84
5 11 11 11 8 8.33 9.26 7.81 8.47
6 14 14 14 8 9.96 8.10 8.84 7.04
> head(anscombe_long)
key x y
1 1 10 8.04
2 2 10 9.14
3 3 10 7.46
4 4 8 6.58
5 1 8 6.95
6 2 8 8.14
৹ඒతνϟωϧ Y࣠
Z࣠
৭
ʹରԠ͢ΔมʹͳΔΑ͏มܗ
anscombe_long <-
pivot_longer(data = anscombe,
cols = everything(),
names_to = c(".value",
"key"),
names_pattern = "(.)(.)")
ԣσʔλ
ॎσʔλ
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ggplot(data = anscombe_long) +
aes(x = x, y = y, color = key) +
geom_point()
ggplot(data = anscombe_long) +
aes(x = x, y = y, color = key) +
geom_point() +
facet_wrap(facets = . ~ key, nrow = 1)
ਫ४ͰਤΛׂ͢Δ
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まとめ
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import Tidy
Transform
Visualize
Model
Communicate
Modified from “R for Data Science”, H. Wickham, 2017
Data Science
①
②
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import Tidy
Transform
Visualize
Model
Communicate
Modified from “R for Data Science”, H. Wickham, 2017
preprocessing
Data science
Data
Observa=on Hypothesis
NarraFve of data
feedback
Data processing
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data.frame / *bble
raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
pivot_wider()
Plot
{ggplot2}
Image Files
ggsave()
Data Processing
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raed_csv()
write_csv()
Table Data
Wide form Long form
pivot_longer()
pivot_wider()
Plot
{ggplot2}
Image Files
ggsave()
Data Processing
Long form
Long form
Long form
Long form
Long form
Long form
Long form
Long form
data.frame / *bble
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It (dplyr) provides simple “verbs” to help
you translate your thoughts into code.
func?ons that correspond to the most
common data manipula?on tasks
Introduc6on to dplyr
h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html
WFSCT {dplyr}
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1. mutate()
2. filter()
3. select()
4. group_by()
5. summarize()
6. left_join()
7. arrange()
Data.frame manipula@on
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import Tidy
Transform
Visualize
Model
Communicate
Modified from “R for Data Science”, H. Wickham, 2017
Data Science
①
②
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#
$
%!
&!
%"
&"
# $
&!
&"
%!
%"
mapping
x axis, y axis, color, fill,
shape, linetype, alpha…
aesthetic channels
data
ggplot2 package
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HHQMPUʹΑΔσʔλՄࢹԽ
࣮ଘ
ࣸ૾ʢ؍ʣ
σʔλ
ࣸ૾ʢσʔλՄࢹԽʣ
άϥϑ
!
"
#!
$!
#"
$"
SBXEBUB
写像
aesthetic channels
৹ඒతνϟωϧ
ՄࢹԽʹదͨ͠EBUBܗࣜ
変形
ਤͷͭͷ৹ඒతνϟωϧ͕
σʔλͷͭͷมʹରԠ͍ͯ͠Δ
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Enjoy!!